1. Field of the Invention
The present general inventive concept relates to encoding and decoding a speech signal, and more particularly, to a method and apparatus to convert a linear predictive coding (LPC) coefficient into a coefficient having order characteristics, such as a line spectrum frequency (LSF), and vector-quantizing the coefficient having the order characteristics.
2. Description of the Related Art
Methods of quantization of prediction error of LSF coefficients can be divided into two types, scalar quantization methods and vector quantization methods. The scalar quantization method quantizes an input signal into a discrete values, and the vector quantization method determines an input signal as a sequence of several related signals and uses a vector as a basic unit of quantization. At present, the vector quantization method is more widely used than the scalar quantization method. Although the vector quantization method uses more bits, it provides better performance as compared to the scalar quantization method.
For high quality speech coding in a speech coding system, it is very important to efficiently quantize linear predictive coding (LPC) coefficients indicating a short interval correlation of a speech signal. In an LPC filter, an optimal LPC coefficient value is obtained so that after an input voice signal is divided into frame units, the energy of a prediction error for each frame is minimized. So far, many methods for efficient quantization of LPC coefficients have been developed and are actually being used in voice compression apparatuses. One of these methods, direct quantization of LPC filter coefficients, has problems in that the characteristic of an LPC filter is too sensitive to quantization errors of LPC coefficients, and stability of the LPC filter after quantization is not guaranteed. Accordingly, LPC coefficients should be converted into other parameters having a good quantization characteristic and then quantized, i.e., reflection coefficients or line spectrum frequency (LSF) coefficients. Moreover, most standard speech coders recently developed utilize the LSF quantization speech coding method since the LSF coefficients are closely associated with speech signal frequency properties of speech signals.
When a speech signal is coded, the speech signal is usually converted into line spectrum frequency (LSF) coefficients, and the LSF coefficients are then quantized. This is because significant changes occur when linear predictive coding (LPC) coefficients themselves are quantized using a small number of bits. Since each LSF coefficient is discretely quantized in the scalar quantization method, at least 32 bits/frames are required to express high speech quality. However, most speech coders operating at 4.8 Kbps do not assign more than 24 bits/frame to each LSF coefficient. Therefore, the vector quantization method is used to reduce the number of bits used.
The vector quantization method achieves effective data compression by creating data as a block and quantizing the data in units of vectors. The vector quantization method is used in a wide range of areas such as image processing, speech processing, facsimile transmission, and meteorological satellites communications. Codebooks indicating data vectors are very important to encode and decode data using the vector quantization method.
It is difficult for such codebooks used in the vector quantization method to provide optimal quantization for LSF coefficients having diverse ranges. In addition, when LSF coefficients in the same range have different average values, quantization efficiency is reduced. Therefore, a more effective way of quantizing and de-quantizing LPC coefficients is needed.